摘要翻译：
尽管利用影响图中的条件独立性，已经开发了许多相关的算法来评估影响图，但对于许多重要问题，精确解仍然是一个难题。本文引入决策电路，利用决策问题中常见的局部结构，提高影响图分析的性能。这项工作建立在概率推理算法的基础上，使用算术电路来表示贝叶斯信念网络[Darwiche，2003]。一旦编译，这些算法电路有效地评估信任网络上的概率查询，并开发了开发网络全局和局部结构的方法。我们证明了判决电路可以以类似的方式构造，并承诺类似的好处。
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英文标题：
《Evaluating influence diagrams with decision circuits》
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作者：
Debarun Bhattacharjya, Ross D. Shachter
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Although a number of related algorithms have been developed to evaluate influence diagrams, exploiting the conditional independence in the diagram, the exact solution has remained intractable for many important problems. In this paper we introduce decision circuits as a means to exploit the local structure usually found in decision problems and to improve the performance of influence diagram analysis. This work builds on the probabilistic inference algorithms using arithmetic circuits to represent Bayesian belief networks [Darwiche, 2003]. Once compiled, these arithmetic circuits efficiently evaluate probabilistic queries on the belief network, and methods have been developed to exploit both the global and local structure of the network. We show that decision circuits can be constructed in a similar fashion and promise similar benefits.
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PDF链接：
https://arxiv.org/pdf/1206.5257